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Automatic segmentation of tissue sections using the multielement information provided by LA-ICP-MS imaging and k-means cluster analysis

Automatic segmentation of tissue sections using the multielement information provided by LA-ICP-MS imaging and k-means cluster analysis,10.1016/j.ijms

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Automatic segmentation of tissue sections using the multielement information provided by LA-ICP-MS imaging and k-means cluster analysis  
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Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) is an established and powerful tool to analyse the distribution of elements in tissue sections. Among other applications, the technique is expected to play a central role in the understanding of normal and pathological element distributions in brain tissue.In order to interpret the distribution of elements such as the bio-metals Cu, Zn, Fe and Mn and proceed to an element-based comparison between groups of samples, it is necessary to anatomically parcel the tissue section into regions-of-interest and to average element signals across these regions. This categorization, also termed segmentation, can be done manually, but the support of automated procedures is highly desirable, especially in order to (1) identify groups of pixels with similar elemental fingerprint, termed clusters, and to determine which degree of discrimination is reasonable; (2) segment anatomical structures known to exhibit substructure but without clearly defined borders, such as the healthy cortex, zones of tumours or ischemic lesions, in an observer-independent way; and (3) to investigate correlation between the distribution of elements in tissue and phenomena which incorporate contributions from several elements in a convoluted way, such as the origin of contrast in magnetic resonance imaging (MRI) experiments.The multi-parametric information provided by LA-ICP-MS lends itself naturally to multivariate analysis. This study provides a new way to synthesise the information distributed over many element images by demonstrating the possibility to segment tissue sections into biologically meaningful substructures. This data-driven, observer-independent categorization was based on k-means clustering. The optimal number of clusters was determined based on the silhouette method.Segmentation of healthy tissue resulted in a set of substructures in perfect congruence to the anatomical architecture. Segmentation of ischemic lesions identified a number of regions with different fingerprints of C, P, Fe, Cu and Zn deposits. Clustering provides a promising way of combining the information present in several element images and reveals structure which is not entirely present in any isolated image.As a useful by-product of this study we have found a promising method for investigating the optimal line length within the process of image reconstruction from the continuous stream of raw data points. Images were characterized by their tensor of inertia, in image- as well as in Fourier dual-space (k-space) and changes in the ratio of the intrinsic moments of inertia or the orientation of the principal axes were found to closely describe the optimum orientation. The first results look very encouraging, but the method must be extensively tested before it can be used as an automatic procedure.In conclusion, cluster analysis of mass spectrometric imaging data allows one to define the fingerprint element distribution of different anatomically or functionally distinct regions and opens a new way for the study of correlation between the element distribution and related phenomena.
Journal: International Journal of Mass Spectrometry - INT J MASS SPECTROM , vol. 307, no. 1, pp. 245-252, 2011
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